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Spiking Neural Network (SNN)

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm

... a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus ...the Neural Engineer- ing Framework ...of neural motor ...

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Spiking Neural Network Decoder for Brain-Machine Interfaces

Spiking Neural Network Decoder for Brain-Machine Interfaces

... a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement ...

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Brian2GeNN: accelerating spiking neural network simulations with graphics hardware

Brian2GeNN: accelerating spiking neural network simulations with graphics hardware

... for spiking neural network simulation, but rather the best algorithm will depend on the ...GPU spiking neural network simulator packages is therefore ...

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Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

... a Spiking Neural Network with Memristive Nanodevices Damien Querlioz, Member, IEEE, Olivier Bichler, Philippe Dollfus, Member, IEEE, and Christian Gamrat Abstract—Memristive nanodevices can feature a ...

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Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

... recurrent spiking neural network with connectivity leading to attractor ...sponding neural subpopulation shows elevated activity, as observed experimentally in dlPFC ...

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Simulation of a memristor-based spiking neural network immune to device variations

Simulation of a memristor-based spiking neural network immune to device variations

... Abstract— We propose a design methodology to exploit adaptive nanodevices (memristors), virtually immune to their variability. Memristors are used as synapses in a spiking neural network performing ...

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A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision

A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision

... a spiking neural network model that solves the depth from focus efficiently by exploit- ing an event-based representation amenable to neuromorphic hardware ...The network operates on visual ...

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Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

... existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and ...

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Exponential stability of the stationary distribution of a mean field of spiking neural network

Exponential stability of the stationary distribution of a mean field of spiking neural network

... but a complete analogy with ( 1 ) would require to introduce unbounded birth / death rates of the species which is less studied for modeling reasons. Another noticeable difference lies in the fact that the equations are ...

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Spiking neural networks based on resistive memory technologies for neural data analysis

Spiking neural networks based on resistive memory technologies for neural data analysis

... by spiking neural network simulations for two ...of Spiking Neural Networks in case of highly noisy input data and thus enables reliable signal ...the network since it behaves as ...

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Stimulus sensitivity of a spiking neural network model

Stimulus sensitivity of a spiking neural network model

... their neural network model thanks to the dynamic range (expressed in decibel and mainly used for sound ...The neural network model used in the present article is known as age dependent Hawkes ...

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Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits

Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits

... architecture: spiking neural network with N inputs = 784 and N outputs = 100 analog CMOS pre- and postsynaptic neurons (numbered circles) fully connected through a single layer of memristive synapses ...

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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks

... artificial neural network (ANN) is trained in a supervised manner using ...matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and arguably the only viable ...

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Spiking Neural Networks modelled as Timed Automata with parameter learning

Spiking Neural Networks modelled as Timed Automata with parameter learning

... cycle-free spiking neural networks and aims at training networks to produce a given output sequence for each class of input ...a network according to the time occurrences of input and output spikes ...

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Back-engineering of spiking neural networks parameters

Back-engineering of spiking neural networks parameters

... the neural network model is a complex ...as spiking neural network are concerned STDP ...the neural network parameters in order to con- straint the neural ...

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EnaS: a new software for neural population analysis in large scale spiking networks

EnaS: a new software for neural population analysis in large scale spiking networks

... From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013 Paris, France. 13-18 July 2013 With the advent of new Multi-Electrode Arrays techni- ques (MEA), the simultaneous recording of the activity up to ...

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Deep convolution neural network for image recognition

Deep convolution neural network for image recognition

... Convolution Neural Network (CNN) architecture in order to classify epidemic pathogen with five deep learning phases: (1) Training dataset of provided images (2) CNN Training (3) Testing data preparation (4) ...

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A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

... the network increases space complexity, it sure decreases learning time but since one advantage of SNNs is to be energy efficient (as well as bio-like), it might be interesting to check the computational tradeoffs ...

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NEURAL NETWORK AND SEGMENTED LABOUR MARKET

NEURAL NETWORK AND SEGMENTED LABOUR MARKET

... The Kohonen classification shown in table 6 (Kohonen, 1984, 1985 ; Cottrell, 1997, see in the Appendix I for some details about the method) is obtained using quantitative [r] ...

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Analysis of a neural network model

Analysis of a neural network model

... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ...

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